Correction: A luminescent bimetallic iridium(iii) complex for ratiometric tracking intracellular viscosity.

Correction for 'A luminescent bimetallic iridium(iii) complex for ratiometric tracking intracellular viscosity' by Fengyu Liu et al., Chem. Commun.

A luminescent bimetallic iridium(iii) complex for ratiometric tracking intracellular viscosity.

A luminescent bimetallic iridium probe C10 was developed through a long soft carbon chain linkage to achieve ratiometric detection of viscosity. C10 features high sensitivity and selectivity for viscosity. More importantly, C10 is living cell permeable and can be employed to distinguish cancer cells from normal cells and track viscosity changes during MCF-7 cell apoptosis.

A water-soluble near-infrared fluorescent probe for specific Pd detection.

Palladium (Pd) is widely used in chemistry, biology, environmental science etc., and Pd is the most plenitudinous oxidation state of the Pd that can exist under physiological conditions or in living cells, which could have adverse effects on both our health and environment. Thus, it is of great significance to monitor the changes of Pd.

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines.

Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs.

Systems pharmacology to decipher the combinational anti-migraine effects of Tianshu formula.

Migraine is the most common neurovascular disorder that imparts a considerable burden to health care system around the world. However, currently there are still no effective and widely applicable pharmacotherapies for migraine patients. Herbal formulae, characterized as multiple herbs, constituents and targets, have been acknowledged with clinical effects in treating migraine, which attract more and more researchers' attention although their exact molecular mechanisms are still unclear.

Systems pharmacology dissection of the anti-inflammatory mechanism for the medicinal herb Folium eriobotryae.

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated.

A diagnostic methodology for Alzheimer's disease.

Background: Like all other neurodegenerative diseases, Alzheimer's disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed.

Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network.

Cancer classification: Mutual information, target network and strategies of therapy.

Background: Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.

Methods: We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification.

Integrative identification of core genetic regulatory modules via a structural model-based clustering method.

Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach.

The tendency to recreate ancestral CG dinucleotides in the human genome.

Background: The CG dinucleotides are known to be deficient in the human genome, due to a high mutation rate from 5-methylated CG to TG and its complementary pair CA. Meanwhile, many cellular functions rely on these CG dinucleotides, such as gene expression controlled by cytosine methylation status. Thus, CG dinucleotides that provide essential functional substrates should be retained in genomes.

Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern.

Background: Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind.

Results: A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks.

Text mining biomedical literature for constructing gene regulatory networks.

In this paper, we present the framework of a Gene Regulatory Networks System: GRNS. The goals of GRNS include automatically mining biomedical literature to extract gene regulatory information (strain number, genotype, gene regulatory relation, and phenotype), automatically constructing gene regulatory networks based on extracted information and integrating biomedical knowledge into the regulatory networks.

Tracking cells in Life Cell Imaging videos using topological alignments.

Background: With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells - many algorithms tend to recognize one cell as several cells or vice versa.

Results: We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence.

DYNAMIK: a software environment for cell DYNAmics, Motility, and Information tracKing, with an application to Ras pathways.

The emergence of new microscopy techniques in combination with the increasing resource of bioimaging data has given fresh impetus to utilizing image processing methods for studying biological processes. Cell tracking studies in particular, which are important for a wide range of biological processes such as embryonic development or the immune system, have recently become the focus of attention. These studies typically produce large volumes of data that are hard to investigate manually and therefore call for an automated approach.

Translational systems genomics: ontology and imaging.

In developing an integrated framework for translational bioinformatics, we consider bioimaging in the NIH Roadmap that exploits high-resolution genomic imaging for clinical applications to the diagnosis and treatment of genetic disorders/diseases. On one hand, we develop new image processing techniques, while on the other, we use the fusion of several well known ontological standards - Gene Ontology (GO), Clinical Bioinformatics Ontology (CBO), Foundational Model of Anatomy (FMA) and Microarry Gene Expression Data Ontology (MGED) in this framework. We have discovered that the heterogeneity of the imaging data can be resolved at the different ontological levels of this framework.

Specificity rule discovery in HIV-1 protease cleavage site analysis.

Several machine learning algorithms have recently been applied to modeling the specificity of HIV-1 protease. The problem is challenging because of the three issues as follows: (1) datasets with high dimensionality and small number of samples could misguide classification modeling and its interpretation; (2) symbolic interpretation is desirable because it provides us insight to the specificity in the form of human-understandable rules, and thus helps us to design effective HIV inhibitors; (3) the interpretation should take into account complexity or dependency between positions in sequences. Therefore, it is necessary to investigate multivariate and feature-selective methods to model the specificity and to extract rules from the model.

Significant positive correlation between the recombination rate and GC content in the human pseudoautosomal region.

This paper establishes that recombination drives the evolution of GC content in a significant way. Because the human P-arm pseudoautosomal region (PAR1) has been shown to have a high recombination rate, at least 20-fold more frequent than the genomic average of approximately 1 cM/Mb, this region provides an ideal system to study the role of recombination in the evolution of base composition. Nine non-coding regions of PAR1 are analyzed in this study.

Neuroproteomics in neurotrauma.

Neurotrauma in the form of traumatic brain injury (TBI) afflicts more Americans annually than Alzheimer's and Parkinson's disease combined, yet few researchers have used neuroproteomics to investigate the underlying complex molecular events that exacerbate TBI. Discussed in this review is the methodology needed to explore the neurotrauma proteome-from the types of samples used to the mass spectrometry identification and quantification techniques available. This neuroproteomics survey presents a framework for large-scale protein research in neurotrauma, as applied for immediate TBI biomarker discovery and the far-reaching systems biology understanding of how the brain responds to trauma.

Statistical distributions of optimal global alignment scores of random protein sequences.

Background: The inference of homology from statistically significant sequence similarity is a central issue in sequence alignments. So far the statistical distribution function underlying the optimal global alignments has not been completely determined.

Results: In this study, random and real but unrelated sequences prepared in six different ways were selected as reference datasets to obtain their respective statistical distributions of global alignment scores.